README.md

---
library_name: transformers
tags:
- mistral-8b
- openassistant
- openassisted-english
- language-modeling
- text-generation
- conversational-ai
license: apache-2.0
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.1
---


# Mistral-8B Instruction-Tuned on OpenAssisted-English

This model is a fine-tuned version of [Mistral-8B](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [OpenAssisted-English](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset using Hugging Face's `transformers` library. The model is optimized for high-quality conversational and instruction-following tasks in English.

---

## Model Details

### Model Description

This model is an instruction-tuned version of the Mistral-8B architecture, fine-tuned specifically to follow human instructions and engage in helpful, safe, and factual conversations. It leverages the OpenAssisted-English dataset, a cleaned and filtered subset from OpenAssistant's OASST1 dataset.

* **Developed by:** Akshay Kumar BM
* **Fine-tuned using:** Hugging Face Transformers
* **Dataset used:** OpenAssisted-English (from OpenAssistant)
* **Model type:** Decoder-only Transformer
* **Language(s):** English
* **License:** Apache 2.0
* **Finetuned from model:** mistralai/Mistral-7B-v0.1

---

## Model Sources

* **Base Model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* **Dataset:** [OpenAssisted-English](https://huggingface.co/datasets/OpenAssistant/oasst1)
* **Library:** Hugging Face Transformers
* **Frameworks:** PyTorch, Accelerate

---

## Uses

### Direct Use

* Conversational AI
* Instruction-following agents
* Text completion and generation
* Chatbot backends
* Question answering

### Downstream Use

* Fine-tuning for specific domains (e.g., legal, medical, education)
* Integration into multi-agent systems or RAG pipelines
* Prompt engineering and prototyping

### Out-of-Scope Use

* Use in high-risk environments (e.g., medical diagnosis, legal decision making) without human oversight.
* Generating misinformation, harmful, offensive, or biased content.
* Any use violating Hugging Face’s or Apache 2.0 licensing terms.

---

## Bias, Risks, and Limitations

Despite being fine-tuned for alignment, the model may:

* Hallucinate facts.
* Reflect biases present in the OpenAssistant dataset.
* Respond unpredictably to adversarial or ambiguous prompts.

### Recommendations

* Always include a human-in-the-loop for sensitive applications.
* Evaluate in domain-specific scenarios before deployment.
* Apply additional safety filters for production use.

---

## How to Get Started

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Akshaykumarbm/OpenAssisted-English-Mistral-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

input_prompt = "Explain quantum computing in simple terms."
inputs = tokenizer(input_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## Training Details

### Training Data

The model was trained on the **OpenAssisted-English** dataset, which includes high-quality, human-annotated instruction-response pairs derived from OpenAssistant’s OASST1 dataset.

* Format: Instruction + Response
* Filters: Language = English, Quality ≥ 3, Assistant messages only
* Size: \~100k samples

### Training Procedure

#### Preprocessing

* Tokenization: BPE tokenizer from Mistral
* Truncation: 4096 tokens
* Format: `<s>[INST] prompt [/INST] response</s>`

#### Hyperparameters

* **Precision:** bf16 mixed precision
* **Batch size:** 512 (global)
* **Epochs:** 15 
* **Optimizer:** AdamW
* **LR Scheduler:** CosineDecay
* **Learning rate:** 2e-5
* **Warmup steps:** 500

#### Compute

* **Hardware:** AMD MI300 
* **Training time:** \~18 hours
* **Frameworks:** PyTorch + Accelerate + DDP

---

## Evaluation

### Testing Data

* Held-out subset from OpenAssisted-English
* Manual eval for coherence, helpfulness, and safety
* Evaluation on MT-Bench and AlpacaEval (optional)

### Metrics

* **Helpfulness Score** (manual): \~7.2/10
* **Toxicity (Perspective API):** <1%
* **BLEU, ROUGE:** Used to compare with gold responses


---

## Technical Specifications

* **Architecture:** Mistral 8B (decoder-only transformer)
* **Tokenizer:** Mistral Tokenizer (32k vocab)
* **Context Length:** 8k tokens
* **Parameters:** \~8.1 billion

---

## Citation

If you use this model, please cite the original Mistral model and OpenAssistant dataset.

```bibtex
@misc{mistral2023,
  title={Mistral 7B},
  author={Mistral AI},
  year={2023},
  url={https://mistral.ai/news/announcing-mistral-7b/}
}

@misc{openassistant2023,
  title = {OpenAssistant Conversations - OASST1},
  author = {OpenAssistant Contributors},
  year = {2023},
  url = {https://huggingface.co/datasets/OpenAssistant/oasst1}
}
```

---

## Contact

* **Author:** Akshay Kumar BM
* **Email:** [akshaykumarbedre.bm@gmail.com](mailto:akshaykumarbedre.bm@gmail.com)
* **GitHub:** [akshaykumarbedre](https://github.com/akshaykumarbedre)
* **Hugging Face:** [akshaykumarbm](https://huggingface.co/akshaykumarbm)

---